Many different medical-concept codes have been developed, including various versions of the International Statistical Classification of Diseases and Related Health Problems (“ICD”), including ICD-9 and ICD-10, as well as the systematized nomenclature of medicine (“SNOMED”). These different types of medical-concept codes provide hierarchical, alpha-numeric medical codewords for each of many different types of pathologies, diagnostics, treatments, and other medically related concepts, generally along with textural annotations and other information, much like books in libraries are encoded using the Dewey Decimal System. Medical codes are widely employed in healthcare-billing services, electronic medical records (“EMRs”), and other types of medically related information that is digitally encoded in electronic, electromagnetic, and electro-optical mass-storage devices and memories, accessed by a variety of different types of electronic data-processing systems, and displayed on various types of electronic display devices. Unfortunately, the different medical-concept codes use different alpha-numeric encodings for codewords, have different hierarchical organizations, and contain codewords that correspond to different sets of underlying concepts. It is often necessary, when processing EMRs, healthcare-billing paperwork, and other medically related information, to translate codewords from one medical-concept code to another. For example, a healthcare clinic may internally use codewords from a first medical-concept code and may need to translate these codewords to corresponding codewords of a second medical-concept code used by an insurance provider in order to facilitate processing of invoices submitted by the healthcare clinic to the insurance provider. In another example, organizations may migrate from one medical-concept code to another, the migration process involving translation of codewords stored in current EMRs and invoices to corresponding codewords of a different medical-concept code to avoid using two different types of electronic medical-data processing systems.
Unfortunately, medical-concept codes are enormous, containing many thousands of different codewords, each potentially related to numerous different underlying medical concepts. Manual translation of medical codes would be far too time-consuming and error-prone to be practical for even low-volume translation of codewords from a first medical-concept code to a second, related medical-concept code. In many cases, erroneous translation can lead to delays, unnecessary costs, and other serious and even life-threatening consequences. Because the codewords of one medical-concept code often do not conceptually align with the codewords of another medical-concept code, medical-code translation is, by nature, inexact and far from straightforward. For these reasons, medical providers, insurance companies, EMR processing companies, and many other organizations involved in medically related fields seek accurate and efficient medical-code translation to facilitate various different types of medically related tasks and operations.